Editor's Note
This research demonstrates that NIR spectroscopy coupled with chemometric analyses can be successfully used for rapid detection of goldenseal adulteration in the marketplace. The study evaluates various chemometric models and indicates that selecting a specific approach to chemometric analysis is critical and the optimal model must be determined on a case-by-case basis in order to achieve useful sensitivity and specificity.
Goldenseal ( Hydrastis canadensis L.) has been a popular herb since the 1970s, with a US market share of over $32 million in 2014. Wild goldenseal has been listed in the Convention on International Trade in Endangered Species for decades. Limits in supply and greed for profit have led to adulteration with similar but more accessible and inexpensive plant materials. Fourier transform near-infrared spectroscopy (FT-NIR) coupled with three different chemometric models, partial least squares (PLS) regression, soft independent modeling of class analogy (SIMCA), and moving window principal component analysis (MW-PCA) provide fast, simple, nondestructive approaches to differentiating pure goldenseal from 4 common pure adulterants (yellow dock, yellow root, coptis, Oregon grape). All three models successfully differentiated authentic goldenseal from adulterants. The models were t-tested for detection of goldenseal intentionally mixed with individual adulterants at 2% to 95% theoretical levels made computationally. The PLS model was unable to detect adulterants mixed with goldenseal at any level. The SIMCA model was the best for detection of yellow root and Oregon grape adulteration in goldenseal, as low as 10%. The MW-PCA model proved best for detection of yellow dock at ≥ 15% and coptis adulteration ≥5% in goldenseal. This study demonstrates that NIR spectroscopy coupled with chemometric analyses is a good tool for industry and investigators to implement for rapid detection of goldenseal adulteration in the marketplace, but also indicates that the specific approach to chemometric analysis must be evaluated and selected on a case-by-case basis in order to achieve useful sensitivity and specificity.